c++ - Multiple tracking in a Video -

i m working on small image processing assignment need track 4 red color object. got how track single one. want know best approach track more 1 point.

there 4 points positioned form rectangle can use shape detection or corner detection detect , track points please see image below..

my naive implementation uses technique described @ opencv bounding boxes tracking of red blobs.

the following helper function used retrieve center of red objects detected:

/* get_positions: function retrieve center of detected blobs.  * largely based on opencv's "creating bounding boxes , circles contours" tutorial.  */ std::vector<cv::point2f> get_positions(cv::mat& image) {     if (image.channels() > 1)     {         std::cout << "get_positions: !!! input image must have single channel" << std::endl;         return std::vector<cv::point2f>();     }      std::vector<std::vector<cv::point> > contours;     cv::findcontours(image, contours, cv::retr_list, cv::chain_approx_simple);      // approximate contours polygons , center of objects     std::vector<std::vector<cv::point> > contours_poly(contours.size());     std::vector<cv::point2f> center(contours.size());     std::vector<float> radius(contours.size());     (unsigned int = 0; < contours.size(); i++ )     {         cv::approxpolydp(cv::mat(contours[i]), contours_poly[i], 5, true );         cv::minenclosingcircle((cv::mat)contours_poly[i], center[i], radius[i]);     }      return center; }    

i wrote code test approach in real-time capturing frames webcam. overall procedure quite similar @dennis described (sorry, coding when submitted answer).

ok, fun begins.

int main() {     // open capture device. webcam id 0:     cv::videocapture cap(0);     if (!cap.isopened())     {         std::cout << "!!! failed open webcam" << std::endl;         return -1;     }      // let's create few window titles debugging purposes     std::string wnd1 = "input", wnd2 = "red objs", wnd3 = "output";         // these hsv values used later isolate red-ish colors     int low_h = 160, low_s = 140, low_v = 50;     int high_h = 179, high_s = 255, high_v = 255;      cv::mat frame, hsv_frame, red_objs;     while (true)     {         // retrieve new frame camera         if (!cap.read(frame))             break;          cv::mat orig_frame = frame.clone();         cv::imshow(wnd1, orig_frame); 

orig_frame:

        // convert bgr frame hsv easier separate colors         cv::cvtcolor(frame, hsv_frame, cv_bgr2hsv);          // isolate red colored objects , save them in binary image         cv::inrange(hsv_frame,                     cv::scalar(low_h,  low_s,  low_v),                     cv::scalar(high_h, high_s, high_v),                     red_objs);          // remove small objects (mostly noises)         cv::erode(red_objs, red_objs, cv::getstructuringelement(cv::morph_rect, cv::size(3, 3)));         cv::dilate(red_objs, red_objs, cv::getstructuringelement(cv::morph_rect, cv::size(7, 7)));          cv::mat objs = red_objs.clone();         cv::imshow(wnd2, objs); 

objs:

        // retrieve vector of points (x,y) location of objects         std::vector<cv::point2f> points = get_positions(objs);          // draw small green circle @ locations educational purposes         (unsigned int = 0; < points.size(); i++)             cv::circle(frame, points[i], 3, cv::scalar(0, 255, 0), -1, 8, 0);          cv::imshow(wnd3, frame); 

        char key = cv::waitkey(33);         if (key == 27) {   /* esc pressed */             //cv::imwrite("out1.png", orig_frame);             //cv::imwrite("out2.png", red_objs);             //cv::imwrite("out3.png", frame);             break;         }     }      cap.release();      return 0; }